descriptor-based similarity analysis
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Hledání optimálního nastavení podmínek chemické analýzy je zpravidla zdlouhavý proces. Tento článek k tomuto účelu navrhuje využití neuronových sítí, zejména ve vztahu k určení optimální podmínek pro analýzu zkoumaných látek s využitím technologie LC/MS/MS a ESI ionizací, a to na základě znalosti jejich základních vlastností, označených jako univerzální deskriptory. Práce se soustředí na nalezení takových podmínek analýzy, kdy dochází k maximalizaci signálu iontu prekurzoru. Práce se zabývá zejména otázkou, zda lze výsledky zjištěné na jednom typu analytu použít k neurální interpolační predikci optimálních podmínek analytů podobných.
The search for the optimal instrumental settings of conditions in chemical analysis is typically a lengthy process. This article proposes the use of neural networks for this purpose, particularly in relation to determining the optimal conditions for the analysis of substances under study using LC/MS/MS and ESI technologies, based on the knowledge of their fundamental properties, referred to as universal descriptors. The work focuses on finding such analysis conditions that maximize the precursor ion signal. The paper specifically addresses the question of whether the results obtained from one type of analyte can be used for neural-interpolated prediction of optimal conditions for similar analytes.
- MeSH
- chemické bojové látky chemie MeSH
- chemické techniky analytické metody MeSH
- chromatografie kapalinová metody MeSH
- hmotnostní spektrometrie s elektrosprejovou ionizací metody MeSH
- hmotnostní spektrometrie metody MeSH
- lidé MeSH
- neuronové sítě MeSH
- organofosfáty * chemie analýza MeSH
- Check Tag
- lidé MeSH
The search for the optimal instrumental settings of conditions in chemical analysis is typically a lengthy process. This article proposes the use of neural networks for this purpose, particularly in relation to determining the optimal conditions for the analysis of substances under study using LC/MS/MS and ESI technologies, based on the knowledge of their fundamental properties, referred to as universal descriptors. The work focuses on finding such analysis conditions that maximize the precursor ion signal. The paper specifically addresses the question of whether the results obtained from one type of analyte can be used for neural-interpolated prediction of optimal conditions for similar analytes.
- MeSH
- chemické bojové látky analýza chemie MeSH
- chemické techniky analytické metody MeSH
- chromatografie kapalinová metody MeSH
- hmotnostní spektrometrie s elektrosprejovou ionizací metody MeSH
- hmotnostní spektrometrie metody MeSH
- lidé MeSH
- neuronové sítě MeSH
- organofosfáty * analýza chemie MeSH
- Check Tag
- lidé MeSH
A series of new tertiary phenothiazine derivatives containing a quinoline and a pyridine fragment was synthesized by the reaction of 1-methyl-3-benzoylthio-4-butylthioquinolinium chloride with 3-aminopyridine derivatives bearing various substituents on the pyridine ring. The direction and mechanism of the cyclization reaction of intermediates with the structure of 1-methyl-4-(3-pyridyl)aminoquinolinium-3-thiolate was related to the substituents in the 2- and 4-pyridine position. The structures of the compounds were analyzed using 1H, 13C NMR (COSY, HSQC, HMBC) and X-ray analysis, respectively. Moreover, the antiproliferative activity against tumor cells (A549, T47D, SNB-19) and a normal cell line (NHDF) was tested. The antibacterial screening of all the compounds was conducted against the reference and quality control strain Staphylococcus aureus ATCC 29213, three clinical isolates of methicillin-resistant S. aureus (MRSA). In silico computation of the intermolecular similarity was performed using principal component analysis (PCA) and hierarchical clustering analysis (HCA) on the pool of structure/property-related descriptors calculated for the novel tetracyclic diazaphenothiazine derivatives. The distance-oriented property evaluation was correlated with the experimental anticancer activities and empirical lipophilicity as well. The quantitative shape-based comparison was conducted using the CoMSA method in order to indicate the potentially valid steric, electronic and lipophilic properties. Finally, the numerical sampling of similarity-related activity landscape (SALI) provided a subtle picture of the SAR trends.
- MeSH
- antibakteriální látky chemie farmakologie MeSH
- fenothiaziny chemie MeSH
- heterocyklické sloučeniny chemie MeSH
- lidé MeSH
- mikrobiální testy citlivosti MeSH
- nádorové buňky kultivované MeSH
- nádory farmakoterapie MeSH
- protinádorové látky chemie farmakologie MeSH
- Staphylococcus aureus účinky léků MeSH
- vztahy mezi strukturou a aktivitou MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
BACKGROUND: Although the etiology of chronic lymphocytic leukemia (CLL), the most common type of adult leukemia, is still unclear, strong evidence implicates antigen involvement in disease ontogeny and evolution. Primary and 3D structure analysis has been utilised in order to discover indications of antigenic pressure. The latter has been mostly based on the 3D models of the clonotypic B cell receptor immunoglobulin (BcR IG) amino acid sequences. Therefore, their accuracy is directly dependent on the quality of the model construction algorithms and the specific methods used to compare the ensuing models. Thus far, reliable and robust methods that can group the IG 3D models based on their structural characteristics are missing. RESULTS: Here we propose a novel method for clustering a set of proteins based on their 3D structure focusing on 3D structures of BcR IG from a large series of patients with CLL. The method combines techniques from the areas of bioinformatics, 3D object recognition and machine learning. The clustering procedure is based on the extraction of 3D descriptors, encoding various properties of the local and global geometrical structure of the proteins. The descriptors are extracted from aligned pairs of proteins. A combination of individual 3D descriptors is also used as an additional method. The comparison of the automatically generated clusters to manual annotation by experts shows an increased accuracy when using the 3D descriptors compared to plain bioinformatics-based comparison. The accuracy is increased even more when using the combination of 3D descriptors. CONCLUSIONS: The experimental results verify that the use of 3D descriptors commonly used for 3D object recognition can be effectively applied to distinguishing structural differences of proteins. The proposed approach can be applied to provide hints for the existence of structural groups in a large set of unannotated BcR IG protein files in both CLL and, by logical extension, other contexts where it is relevant to characterize BcR IG structural similarity. The method does not present any limitations in application and can be extended to other types of proteins.
In order to provide a more detailed view on the structure⁻antimycobacterial activity relationship (SAR) of phenylcarbamic acid derivatives containing two centers of protonation, 1-[2-[({[2-/3-(alkoxy)phenyl]amino}carbonyl)oxy]-3-(dipropylammonio)propyl]pyrrolidinium oxalates (1a⁻d)/dichlorides (1e⁻h) as well as 1-[2-[({[2-/3-(alkoxy)phenyl]amino}carbonyl)oxy]-3-(di-propylammonio)propyl]azepanium oxalates (1i⁻l)/dichlorides (1m⁻p; alkoxy = butoxy to heptyloxy) were physicochemically characterized by estimation of their surface tension (γ; Traube's stalagmometric method), electronic features (log ε; UV/Vis spectrophotometry) and lipophilic properties (log kw; isocratic RP-HPLC) as well. The experimental log kw dataset was studied together with computational logarithms of partition coefficients (log P) generated by various methods based mainly on atomic or combined atomic and fragmental principles. Similarities and differences between the experimental and in silico lipophilicity descriptors were analyzed by unscaled principal component analysis (PCA). The in vitro activity of compounds 1a⁻p was inspected against Mycobacterium tuberculosis CNCTC My 331/88 (identical with H37Rv and ATCC 2794, respectively), M.tuberculosis H37Ra ATCC 25177, M.kansasii CNCTC My 235/80 (identical with ATCC 12478), the M.kansasii 6509/96 clinical isolate, M.kansasii DSM 44162, M. avium CNCTC My 330/80 (identical with ATCC 25291), M.smegmatis ATCC 700084 and M. marinum CAMP 5644, respectively. In vitro susceptibility of the mycobacteria to reference drugs isoniazid, ethambutol, ofloxacin or ciprofloxacin was tested as well. A very unique aspect of the research was that many compounds from the set 1a⁻p were highly efficient almost against all tested mycobacteria. The most promising derivatives showed MIC values varied from 1.9 μM to 8 μM, which were lower compared to those of used standards, especially if concerning ability to fight M.tuberculosis H37Ra ATCC 25177, M.kansasii DSM 44162 or M. avium CNCTC My 330/80. Current in vitro biological assays and systematic SAR studies based on PCA approach as well as fitting procedures, which were supported by relevant statistical descriptors, proved that the compounds 1a⁻p represented a very promising molecular framework for development of 'non-traditional' but effective antimycobacterial agents.
- MeSH
- antituberkulotika chemická syntéza farmakologie MeSH
- azepiny chemická syntéza farmakologie MeSH
- ciprofloxacin chemie terapeutické užití MeSH
- ethambutol chemie terapeutické užití MeSH
- fenylkarbamáty chemická syntéza farmakologie MeSH
- isoniazid chemie terapeutické užití MeSH
- mikrobiální testy citlivosti MeSH
- Mycobacterium avium účinky léků MeSH
- Mycobacterium kansasii účinky léků MeSH
- Mycobacterium smegmatis účinky léků MeSH
- Mycobacterium tuberculosis účinky léků MeSH
- Mycobacterium účinky léků MeSH
- ofloxacin chemie terapeutické užití MeSH
- oxaláty chemie farmakologie MeSH
- počítačová simulace MeSH
- pyrrolidiny chemická syntéza farmakologie MeSH
- racionální návrh léčiv MeSH
- rozpustnost MeSH
- vztahy mezi strukturou a aktivitou MeSH
- Publikační typ
- časopisecké články MeSH
Motivation: Whole genome expression profiling of large cohorts of different types of cancer led to the identification of distinct molecular subcategories (subtypes) that may partially explain the observed inter-tumoral heterogeneity. This is also the case of colorectal cancer (CRC) where several such categorizations have been proposed. Despite recent developments, the problem of subtype definition and recognition remains open, one of the causes being the intrinsic heterogeneity of each tumor, which is difficult to estimate from gene expression profiles. However, one of the observations of these studies indicates that there may be links between the dominant tumor morphology characteristics and the molecular subtypes. Benefiting from a large collection of CRC samples, comprising both gene expression and histopathology images, we investigated the possibility of building image-based classifiers able to predict the molecular subtypes. We employed deep convolutional neural networks for extracting local descriptors which were then used for constructing a dictionary-based representation of each tumor sample. A set of support vector machine classifiers were trained to solve different binary decision problems, their combined outputs being used to predict one of the five molecular subtypes. Results: A hierarchical decomposition of the multi-class problem was obtained with an overall accuracy of 0.84 (95%CI=0.79-0.88). The predictions from the image-based classifier showed significant prognostic value similar to their molecular counterparts. Contact: popovici@iba.muni.cz. Availability and Implementation: Source code used for the image analysis is freely available from https://github.com/higex/qpath . Supplementary information: Supplementary data are available at Bioinformatics online.
- MeSH
- kolorektální nádory diagnóza genetika metabolismus patologie MeSH
- lidé MeSH
- nádorové biomarkery * MeSH
- neuronové sítě * MeSH
- počítačové zpracování obrazu metody MeSH
- prognóza MeSH
- regulace genové exprese u nádorů MeSH
- support vector machine MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
